from __future__ import annotations

from collections import Counter
from random import random




class 					_UpperCAmelCase			:



 '''simple docstring'''




 def __init__(		self				:   List[str])						->	Any:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   str)						->	None:


   """simple docstring"""







   _UpperCamelCase           =    {}







 def 		__UpperCAmelCase				(		self				:   List[str]							, lowercase_				:   str							, lowercase_				:   str							, lowercase_				:   float)						->	None:


   """simple docstring"""







   if nodea not in self.connections:
     self.add_node(lowercase_)
   if nodea not in self.connections:
     self.add_node(lowercase_)
   _UpperCamelCase           =    probability







 def 		__UpperCAmelCase				(		self				:   Any)						->	list[str]:


   """simple docstring"""







   return list(self.connections)







 def 		__UpperCAmelCase				(		self				:   Tuple							, lowercase_				:   str)						->	str:


   """simple docstring"""







   _UpperCamelCase           =    0
   _UpperCamelCase           =    random()

   for dest in self.connections[node]:
     current_probability += self.connections[node][dest]
     if current_probability > random_value:
       return dest
   return ""


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->dict[str, int]:




  '''simple docstring'''

  _UpperCamelCase           =    MarkovChainGraphUndirectedUnweighted()

  for nodea, nodea, probability in transitions:
    graph.add_transition_probability(a__    ,     a__    ,     a__ )

  _UpperCamelCase           =    Counter(graph.get_nodes() )
  _UpperCamelCase           =    start

  for _ in range(a__ ):
    _UpperCamelCase           =    graph.transition(a__ )
    visited[node] += 1

  return visited


if __name__ == "__main__":
   import doctest

   doctest.testmod()





import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''image_processor''', '''tokenizer''']
 __A			       =					'''ViTImageProcessor'''
 __A			       =					('''CLIPTokenizer''', '''CLIPTokenizerFast''')







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]=None							, lowercase_				:   Dict=None							, **lowercase_				:   List[str])						->	str:


   """simple docstring"""







   _UpperCamelCase           =    None
   if "feature_extractor" in kwargs:
     warnings.warn(
         "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
         " instead."							, lowercase_							, )
     _UpperCamelCase           =    kwargs.pop("feature_extractor")

   _UpperCamelCase           =    image_processor if image_processor is not None else feature_extractor
   if image_processor is None:
     raise ValueError("You need to specify an `image_processor`.")
   if tokenizer is None:
     raise ValueError("You need to specify a `tokenizer`.")

   super().__init__(lowercase_							, lowercase_)







 def __call__(		self				:   Any							, lowercase_				:   List[str]=None							, lowercase_				:   int=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Dict=None							, **lowercase_				:   Dict)						->	int:


   """simple docstring"""







   if text is None and visual_prompt is None and images is None:
     raise ValueError("You have to specify either text, visual prompt or images.")

   if text is not None and visual_prompt is not None:
     raise ValueError("You have to specify exactly one type of prompt. Either text or visual prompt.")

   if text is not None:
     _UpperCamelCase           =    self.tokenizer(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if images is not None:
     _UpperCamelCase           =    self.image_processor(lowercase_							, return_tensors=lowercase_							, **lowercase_)

   if visual_prompt is not None and images is not None:
     _UpperCamelCase           =    {
         "pixel_values": image_features.pixel_values,
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   elif text is not None and images is not None:
     _UpperCamelCase           =    image_features.pixel_values
     return encoding
   elif text is not None:
     return encoding
   elif visual_prompt is not None:
     _UpperCamelCase           =    {
         "conditional_pixel_values": prompt_features.pixel_values,
     }
     return encoding
   else:
     return BatchEncoding(data=dict(**lowercase_)							, tensor_type=lowercase_)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, *lowercase_				:   List[Any]							, **lowercase_				:   List[Any])						->	int:


   """simple docstring"""







   return self.tokenizer.batch_decode(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, *lowercase_				:   Any							, **lowercase_				:   int)						->	Optional[int]:


   """simple docstring"""







   return self.tokenizer.decode(*lowercase_							, **lowercase_)







 @property
 def 		__UpperCAmelCase				(		self				:   Optional[int])						->	str:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							, lowercase_							, )
   return self.image_processor_class







 @property
 def 		__UpperCAmelCase				(		self				:   int)						->	int:


   """simple docstring"""







   warnings.warn(
       "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							, lowercase_							, )
   return self.image_processor




